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深度MS/MS频谱预测的测试时间训练改善了的识别

Jianbai Ye¹, Xiangnan He¹, Shujuan Wang²

¹MoE Key Laboratory of Brain-inspired Intelligent Perception and Cognition, University of Science and Technology of China, Hefei, Anhui 230026, China.

Journal of proteome research

|December 28, 2023

概括

蛋白质学中的深度学习可以通过测试时间训练 (PepT3) 改善的识别. 这种方法通过调整模型以适应特定的实验数据来提高准确性,从而促进了免疫学方面的发现.

科学领域:

蛋白质组学是指蛋白质组学.
生物信息学是一种生物信息学.
计算生物学计算生物学

背景情况:

基光谱匹配对于在自下而上的蛋白质组学中识别基和蛋白质至关重要.
深度学习模型可以预测质谱的质谱匹配,但在数据变化方面存在困难.
样本类型,酶特异性和仪器校准的差异导致标准监督学习模型的预测不准确.

研究的目的:

开发一种用于改善蛋白质组学中的标识的新方法.
解决一般深度学习模型在处理实验数据变异方面的局限性.
介绍PepT3,一个测试时间训练范式,用于适应性-频谱匹配.

主要方法:

开发了PepT3,一个测试时间训练范式,以适应预训练的深度学习模型.
实施了自适应建模,以生成实验数据特定的模型来匹配光谱.
将PepT3应用于标准和患者衍生的免疫型样本.

主要成果:

根据数据的可变性,PepT3增加了10-40%的标识.
在免疫类药物中,PepT3发现了60%的瘤特异性免疫候选者.
预计三分之二的新发现的候选物会与患者的人类白细胞抗原异型结合.

结论:

测试时间训练 (PepT3) 在蛋白质组学中显著增强了和蛋白质的识别.
PepT3提高了瘤特异性免疫的发现,这对癌症研究至关重要.
PepT3模型和结果在Zenodo.org上公开存档 (标识符为8231084).

关键词:

已经不再分销的产品.酸标识酸标识质谱匹配的匹配频谱预测频谱预测测试时间培训培训

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